Simulation-Based Validation of Autonomous Vehicle Algorithms

Abstract

The growing complexity of autonomous vehicle (AV) systems necessitates advanced validation methodologies to ensure safety, reliability, and efficiency before real-world deployment. This paper explores simulation-based validation (SBV) as a core approach for testing and verifying AV algorithms under diverse and controlled virtual environments. The study investigates the architecture of simulation platforms, scenario generation, perception models, and control algorithms. By integrating machine learning-driven simulations with realistic traffic dynamics and sensor data, the research provides insights into improving model fidelity and reducing real-world risks. The paper also discusses performance metrics, validation frameworks, and the future of simulation-based testing in autonomous mobility